A Multi-Model Approach to Academic Performance Prediction Using Student–Semester Educational Data
Abstract
Identifying students at academic risk early calls for forecasting that respects the temporal structure of educational data and stays readable for advising staff. In this work, linear, gradient-boosted, and deep tabular models are compared under one protocol on two tasks: next-semester performance prediction and binary risk classification. The data are aggregated student–semester records from a university Learning Management System, covering 732,965 academic records on 20,868 students over 14 academic years at a Central Asian institution. All models are evaluated under a strict temporal train–validation–test split, so each is asked to forecast a year it has never seen. The feature set adds two trajectory variables (avg_mark_lag1, delta_avg) for the direction of a student’s performance and two within-semester indicators (std_mark, min_mark) for its stability. On the 2022 test set, Ridge Regression reached R² = 0.552 and Logistic Regression PR-AUC = 0.432 (ROC-AUC = 0.904). The gradient-boosted ensembles (LightGBM, CatBoost, XGBoost) did not clearly beat the regularized linear baselines under temporal validation, and the deep tabular architectures (FT-Transformer, TabNet, AutoInt, MLP-embeddings, Deep & Cross), run under the identical protocol, did not outperform either family. A Top-K intervention rule recovers 33% of all at-risk students within the top 5% of ranked cases (lift ≈ 4.15×). That is a decision rule an advising office can work with under a fixed caseload. Received: 15 May 2026 | Revised: 11 August 2026 | Accepted: 30 August 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/MMR000/Smart_EDU_Score_Predict. Author Contribution Statement Tole Bi Yermek: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft. Banu Yergesh: Methodology, Validation, Formal analysis. Amanzhol Yelemessov: Methodology, Software, Data curation, Visualization. Madina Yelemessova: Validation, Investigation, Data curation, Visualization. Gulmira Bekmanova: Investigation, Writing – review & editing. Assel Omarbekova: Resources, Project administration, Funding acquisition. Aizhan Nazyrova: Conceptualization, Writing – original draft, Supervision. Zhanar Lamasheva: Writing – review & editing, Supervision.